Fuzzy logic-based approach for porcine DNA determination in meat products

Ensuring halal food integrity requires detecting porcine DNA, as contamination may occur during processing despite the use of halal-certified ingredients. This study presents a fuzzy logic (FL) framework to determine the presence of porcine DNA in processed meat by analyzing cycle threshold (Ct) values of four target genes: Cytochrome B, 18 s RNA, 12 s RNA, and D-loop mitochondria. The framework utilizes 12 fuzzy rules, 12 input functions, and 5 output functions, validated with 48 data points from laboratory and historical analyses. A key innovation is the graphical user interface (GUI), enhancing usability for researchers, auditors, and food industry professionals. The GUI allows users to input data effortlessly and receive instant results without technical expertise. If Ct values are below 40, the system confirms the presence of porcine DNA; otherwise, it indicates its absence. The FL framework accelerates detection, improves accuracy, and strengthens halal certification processes. Future enhancements include integrating AI models for higher precision and developing sensor-based real-time detection. This framework supports food safety regulations and ensures compliance with halal standards, providing a reliable, accessible, and efficient tool for the food industry.